Concern 7 · Working draft · editorial review in progress
Geographic bias
Geographic bias means that a system's examples or assumptions fit some places better than the places where it is used.
Understanding the concern
Data collected in a few countries or cities can encode their roads, buildings, customs, and institutions. A system may then misread a familiar task elsewhere, without warning that its knowledge is uneven.
A location label alone will not solve this. Teams need to test local conditions and involve people who understand them. Success in one city is evidence about that setting, not automatic proof of success worldwide.
An example
An image tool trained on kitchens in wealthy countries may struggle to recognize a kitchen with different cooking equipment.
Illustrative unless identified as a documented case.
Evidence and its limits
Supports examining data origins and source composition; does not establish that every model has the same geographic skew.
Gebru and colleagues · Communications of the ACM · Research proposal
Datasheets for Datasets ↗ (opens in a new tab)Documentation questions cover composition, collection, time, uses, permissions, and maintenance. This is a documentation method, not proof of a particular dataset's defects.
Dodge and colleagues · EMNLP 2021 · Dataset audit
Documenting Large Webtext Corpora: A Case Study on C4 ↗ (opens in a new tab)Examines sources and filters in C4, including disproportionate removal of minority-associated material. Findings concern this corpus and process.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Has this tool been tested in the places where people will actually use it?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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